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7 Data Integration Techniques

In the last couple of years, technological advancements have contributed to the growth of data volumes in businesses across all industry verticals, as most businesses now use multiple applications such as ERP, CRM, WMS, and MDM to name just a few. The downside is that data is locked within individual applications, resulting in disconnects and miscommunications in the workflow processes and between the departments in the business.


Decisions made by the businesses based on this data will be less optimal and even detrimental to the intended outcome. Therefore, data integration is a critical component that all businesses should embrace and implement.


Here are the core 7 data integration techniques:


Hand Coding/Manual Data Integration

Generally used for integrating a small number of data sources, hand-coding is among the most basic methods for integrating data. The cons of this method are that it takes a considerable amount of time, and scaling to integrate more data sources is difficult.


Data Warehousing

Data warehousing is sometimes referred to as common storage integration. It is a data management system designed to enable and support business intelligence (BI) activities such as analytics. Data warehouses are usually designed for querying and analyzing large amounts of historical data.


Querying data on a data warehouse instead of on the source application allows analysts to avoid worrying about impacting an application’s performance and ability to view all of an organization’s data in a single, central location. This makes it easy to check for data completeness, accuracy, and consistency.


Middleware Data Integration

The purpose of middleware is generally integration. Therefore, Middleware data integration refers to a data integration system that uses a middleware application as a go-between that moves data between source systems and a central data repository. The middleware formats and validates the data before sending it to the repository, a cloud data warehouse, or an on-premises database.


Data consolidation

Data consolidation involves combining data from multiple sources or systems to create a single, centralized data source, which can then be used for reporting or analytics by ETL (Extract, Transform, Load) or ELT (Extract, Load and Transform) applications. You could experience some latency in data consolidation, which is caused by the process of having to retrieve from multiple sources, though more frequent data transfers can shorten the latency period.


Among the many benefits of data, consolidation is that data workers get the chance to improve data quality and integrity since the data is transformed before it is consolidated. Care has been taken to keep it in a consistent format on the central data source.


Data virtualization

Data virtualization is a logical layer that integrates data from a wide range of source systems, delivers it in real-time, and provides a unified view while the data remains in its separate source systems.


Data virtualizations save on storage costs as you don’t have to move your data around.


Data federation

Data federation is a software process that allows multiple databases to function as one. It creates a virtual database that consolidates data from disparate sources. Users can then use this virtual database as a single point of reference for all data in the organization. This is how it works: when a user queries the virtual database, the query is sent to the relevant underlying data source, returning the requested data.


Data propagation

Data propagation refers to applications to copy data from one location to another on an event-driven basis. Generally, technologies used for data propagation are; Enterprise Application Integration (EAI) and Enterprise Data Replication (EDR). EAI provides a link between two systems used for business transaction processing. Businesses frequently use EDR, and it does not perform any data transformation, for the data is extracted from one database and moved to another one.


Want to learn More About Data Integration?

Due to the complexity of data, the speed and scale required to handle data integrations cost-effectively are in the cloud. Distilled Data provides complete data integration and transformation solutions purpose-built for the cloud with our industry leading products, like NIRVANA, for NetSuite integrations.


Request a free Proof of Concept to learn more about how you can unlock the potential of your data with the Distilled Data's Nirvana SaaS iPaaS integration solution.



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